Chapter Preview
TopMain Focus
Modern metaheuristic algorithms have been developed with an aim to carry out global search with three main purposes: solving problems faster, solving large problems, and obtaining robust algorithms. The efficiency of metaheuristic algorithms can be attributed to the fact that they imitate the best features in nature, especially the selection of the fittest in biological systems which have evolved by natural selection over millions of years. Cuckoo search is a new metaheuristic search algorithm which is based on the obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds and fruit flies. It is developed by Yang and Deb (2009) and the preliminary studies show that it is very promising and could outperform existing algorithms such as genetic algorithms and particle swarm optimization (Gandomi et al., 2013).
Key Terms in this Chapter
Metaheuristic: A computational method to find optimal solutions by iteratively improving candidate solutions under a pre-determined measure of quality or time.
Lévy Flight: A random walk based process that is characterized by a series of instantaneous jumps chosen from a probability density function which has a power law tail.
Swarm Intelligence: Collective behaviors of ants, fishes, birds, bees and other social instincts or animals.
Cuckoo Search: An algorithm inspired by the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of host birds.
Optimization: Using different solution methods to find best or near optimal solutions (with minimum cost or maximum performance) under the effect of controllable factors (inputs and outputs) in an acceptable time for quantitative problems.
Artificial Intelligence: Intelligent searching methods (learning algorithms, neural networks…) used for optimization.
Bio-Inspired Algorithms: Nature inspired algorithms used for solving problems that imitate the way nature performs (strategy of nature).